"""simple docstring"""



# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    {
           "en": "Machine learning is great, isn't it?",
           "ru": "Машинное обучение - это здорово, не так ли?",
           "de": "Maschinelles Lernen ist großartig, nicht wahr?",
       }

       # BLUE scores as follows:
       # "pair": [fairseq, transformers]
       __a       =    {
           "wmt16-en-de-dist-12-1": [28.3, 27.52],
           "wmt16-en-de-dist-6-1": [27.4, 27.11],
           "wmt16-en-de-12-1": [26.9, 25.75],
       }
       __a       =    F"{src_lang}-{tgt_lang}"

       __a       =    F"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
       model_card_dir.mkdir(parents=a   ,    exist_ok=a				)
       __a       =    os.path.join(a   ,    "README.md"				)
       print(F"Generating {path}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(a				)


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__:Dict									=			repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
   SCREAMING_SNAKE_CASE__:str									=			model_cards_dir / """allenai""" / model_name
   write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)

"""simple docstring"""



from __future__ import annotations

import math



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    u
       for i in range(1   ,    a				):
              __a       =    temp * (u - i)
       return temp



def 	_lowerCamelCase(	):
       __a       =    int(input("enter the numbers of values: "				)				)
       __a       =    []
       for _ in range(a				):
              y.append([]				)
       for i in range(a				):
              for j in range(a				):
                     y[i].append(a				)
                     __a       =    0

       print("enter the values of parameters in a list: "				)
       __a       =    list(map(a   ,    input().split()				)				)

       print("enter the values of corresponding parameters: "				)
       for i in range(a				):
              __a       =    float(input()				)

       __a       =    int(input("enter the value to interpolate: "				)				)
       __a       =    (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1   ,    a				):
              for j in range(n - i				):
                     __a       =    y[j + 1][i - 1] - y[j][i - 1]

       __a       =    y[0][0]
       for i in range(1   ,    a				):
              summ += (ucal(a   ,    a				) * y[0][i]) / math.factorial(a				)

       print(F"the value at {value} is {summ}"				)


if __name__ == "__main__":
   main()
